postprocessor.core.functions.tracks

Functions to process, filter and merge tracks.

Functions

clean_tracks(tracks[, min_len, min_gr])	Clean small non-growing tracks and return the reduced dataframe
get_avg_gr(track)	Get average growth rate for a track.
get_avg_grs(tracks)	Get average growth rate for a group of tracks
get_closest_pairs(pre, post[, tol])	Calculate a cost matrix the Hungarian algorithm to pick the best set of options
get_contiguous_pairs(tracks)	Get all pair of contiguous track ids from a tracks dataframe.
get_dMetric(pre, post[, tol])	Calculate a cost matrix
get_dMetric_wrap(lst, **kwargs)
get_joinable(tracks[, smooth, tol, window, ...])	Get the pair of track (without repeats) that have a smaller error than the tolerance.
get_joint_ids(merging_seqs)	Convert a series of merges into a dictionary where the key is the cell_id of destination and the value a list of the other track ids that were merged into the key
get_last_i(x, i)
get_means(x, i)
get_tracks_ntps(tracks)
get_val(x, n)
get_vec_closest_pairs(lst, **kwargs)
join_track_pair(target, source)
join_tracks(tracks, joinable_pairs[, drop])	Join pairs of tracks from later tps towards the start.
load_test_dset()
localid_to_idx(local_ids, contig_trap)	Fetch then original ids from a nested list with joinable local_ids
max_nonstop_ntps(track)
max_ntps(track)
merge_tracks(tracks[, drop])	Join tracks that are contiguous and within a volume threshold of each other
plot_joinable(tracks, joinable_pairs)	Convenience plotting function for debugging and data vis
rec_bottom(d, k)
solve_matrices(dMetric, prepost[, tol])	Solve the distance matrices obtained in get_dMetric and/or merged from independent dMetric matrices
solve_matrices_wrap(dMetric, edges, **kwargs)
solve_matrix(dMetric)	Solve cost matrix focusing on getting the smallest cost at each iteration.